1. Disease Growth rate

2. Status of cumulative cases

## Warning: Ignoring 8 observations
## Warning: Ignoring 9 observations

3. Geographic distribution of cases

#Read Provincial COVID-19 distribution data
prov <- read.csv("CasebyProvince.csv")

#Read provincial map of Indonesia
province <- geojson_read("IDN_adm_1_province.json", what='sp')

#Prepare data frames for merging the two loaded datasets
slotNames(province)
## [1] "data"        "polygons"    "plotOrder"   "bbox"        "proj4string"
province@data$rn <- row.names(province)

temp.province<-data.table(province@data)

#Fix names of provinces according to those in the map data
prov %>%
  mutate(NAME_1=as.character(Province_name)) -> prov
prov%>%
  mutate(NAME_1=ifelse(NAME_1=="Daerah Istimewa Yogyakarta", 'Yogyakarta', NAME_1),
         NAME_1=ifelse(NAME_1=="DKI Jakarta", 'Jakarta Raya', NAME_1),
         NAME_1=ifelse(NAME_1=="Papua Barat", 'Irian Jaya Barat', NAME_1),
         NAME_1=ifelse(NAME_1=="Kepulauan Bangka Belitung", 'Bangka-Belitung', NAME_1)) -> prov
prov <- prov %>% filter(NAME_1 != 'Indonesia'&NAME_1 !='Kepulauan Riau')

#Merge two datasets
out.prov<-merge(temp.province, prov, by="NAME_1", all.x=TRUE)

out.prov<-data.table(out.prov)

setkey(out.prov, rn)
province@data <- out.prov[row.names(province)]

#Define coloring function and color palette
pal <- colorNumeric("YlOrRd", domain = NULL)

#Plot confirmed cases choropleth
leaflet(data = province)%>%
  addProviderTiles("CartoDB.Positron")%>%
  addPolygons(fillColor = ~pal(log(Confirmed_cases)),
              fillOpacity = 0.8,
              color = "black",
              weight = 1,
              popup=~paste(NAME_1, ", No. of Confirmed cases:", as.character(Confirmed_cases)))%>%
  addMarkers(lng=106.816666,
             lat=-6.200000,
             popup=paste("Jakarta", ", No. of Confirmed cases: 5688"))%>%
  addLegend("bottomleft",
            colors=brewer.pal(5,"YlOrRd"),
            labels=c("low","","","","high"),
            title="Number of Confirmed cases")

4. Fatality rates by province

leaflet(data = province)%>%
  addProviderTiles("CartoDB.Positron")%>%
  addPolygons(fillColor = ~pal((Death_cases/Confirmed_cases)*100),
              fillOpacity = 0.8,
              color = "black",
              weight = 1,
              popup=~paste(NAME_1, ", Case Fatality Rate:", as.character(round((Death_cases/Confirmed_cases)*100, digits=2)), '%'))%>%
  addMarkers(lng=106.816666,
             lat=-6.200000,
             popup=paste("Jakarta", ", Case Fatality Rate: 7.95%"))%>%
  addLegend("bottomleft",
            colors=brewer.pal(5,"YlOrRd"),
            labels=c("low","","","","high"),
            title="Case Fatality rate")

5. Comparison between countries and world average

#Read and fix structure of columns in the global death rate dataset
w_death <- read.csv("deathrate.csv")
w_death$Entity <- as.character(w_death$Entity)
w_death$Date <- mdy(w_death$Date)

#Filter to regions and dates of interest
w_death <- w_death %>% filter(Entity %in% c("World", "United States", "Indonesia", "Asia", "Italy")&
                                Date>=ymd("2020-03-16")&Date<=ymd("2020-05-14"))

#Plot death rate data of different regions
ggplot(w_death)+
  geom_line(aes(x=Date, y=Case.fatality.rate.of.COVID.19......Only.observations.with.â..100.cases....., color=Entity))+
  ggtitle("Case Fatality rate in different countries as of May 14th 2020")+
  ylab("Case Fatality rate (%)")

Literary Sources:

Indonesia Age structure. (n.d.). Retrieved from https://www.indexmundi.com/indonesia/age_structure.html

Jakarta Post. (n.d.). Indonesia’s latest official COVID-19 figures. Retrieved from https://www.thejakartapost.com/news/2020/03/23/indonesias-latest-covid-19-figures.html

Policy Responses to COVID19. (n.d.). Retrieved from https://www.imf.org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19